Algorithmic Bias: The Unseen Student

Context

Your company's AI educational tools are revolutionizing classrooms. However, whispers of bias are turning into shouts. Students from minority backgrounds are being unfairly flagged. Specifically, incorrectly labeling Black and Latinx students as high risk, potentially denying them access to advanced programs. The school board is watching closely.

Dilemma

Do you:
A) Release the tools now, capturing the market and maximizing profits, knowing some students will be disadvantaged? Or
B) Delay the release, invest heavily in fixing the bias, risking losing valuable market share, but ensuring fairness for all students?

Summary

A study using nationally representative data from the Education Longitudinal Study of 2002 and various machine learning modeling approaches revealed significant racial bias in college student success predictions. Algorithms, increasingly used by universities, show less accuracy for racially minoritized students. Attempts to mitigate this bias proved largely ineffective, highlighting how these tools perpetuate existing societal inequalities rather than providing objective assessments.

Resources:

• https://journals.sagepub.com/doi/10.1177/23328584241258741
• https://themarkup.org/machine-learning/2021/03/02/major-universities-are-using-race-as-a-high-impact-predictor-of-student-success